refactor: 重构项目配置管理，统一使用.env配置

- 新增config.py统一读取.env配置，移除硬编码路径和参数
- 重构collect_jrxml.py支持命令行参数和环境变量配置源目录
- 新增.env.example示例配置文件，整理所有可配置项
- 重构down_embedding_model.py、import_to_chroma.py等所有脚本使用统一配置
- 新增Windows一键部署脚本setup.bat
- 修正jrxml_banch_chunker.py的文件名拼写错误

.env.example

@@ -0,0 +1,54 @@
+# ============================================================
+# JRXML RAG 项目 - 环境配置文件
+# 复制此文件为 .env 并根据需要修改配置
+# ============================================================
+
+# -------------------- 嵌入模型配置 --------------------
+# 模型名称或路径，支持以下格式：
+#   1. HuggingFace Hub 模型: Qwen/Qwen3-Embedding-4B
+#   2. HuggingFace Hub 模型: sentence-transformers/all-MiniLM-L6-v2
+#   3. 本地模型路径: models/Qwen3-Embedding-4B
+EMBEDDING_MODEL_NAME=Qwen/Qwen3-Embedding-4B
+
+# 本地模型下载/存放目录（使用 Hub 模型时会自动下载到此目录）
+EMBEDDING_MODEL_PATH=models/Qwen3-Embedding-4B
+
+# HuggingFace 镜像站点（国内用户建议使用镜像加速）
+HF_ENDPOINT=https://hf-mirror.com
+
+# -------------------- 硬件配置 --------------------
+# 是否使用 GPU 加速 (true/false)
+USE_GPU=true
+
+# 是否启用 FP16 半精度（可节省约 50% 显存）
+USE_FP16=true
+
+# 向量化批处理大小（根据显存调整，显存不足时减小此值）
+BATCH_SIZE=64
+
+# -------------------- 目录配置 --------------------
+# JRXML 源文件目录
+JRXML_SOURCE_DIR=jrxml_source
+
+# 分块输出目录
+CHUNKER_OUTPUT_DIR=jrxml_chunker_output
+
+# 向量输出目录
+EMBEDDINGS_DIR=embeddings
+
+# Chroma 向量数据库目录
+CHROMA_DB_PATH=chroma_db
+
+# Chroma 集合名称
+CHROMA_COLLECTION_NAME=jrxml_chunks
+
+# -------------------- 分块配置 --------------------
+# 单个 chunk 最大字符数
+MAX_CHUNK_SIZE=2000
+
+# -------------------- 查询配置 --------------------
+# 默认返回结果数
+DEFAULT_N_RESULTS=5
+
+# 相似度阈值 (0~1，余弦距离，越小越相似)
+SIMILARITY_THRESHOLD=0.3

collect_jrxml.py

@@ -2,10 +2,12 @@
 """
 JRXML 文件收集脚本
 从指定目录递归查找所有 .jrxml 文件并复制到项目的 jrxml_source 目录
+源目录和目标目录通过 .env / config.py 配置
 """
 
 import os
 import shutil
+from config import JRXML_SOURCE_DIR
 
 def collect_jrxml_files(source_dir: str, target_dir: str) -> int:
     """
@@ -53,12 +55,22 @@ def collect_jrxml_files(source_dir: str, target_dir: str) -> int:
     return copied_count
 
 if __name__ == "__main__":
-    SOURCE_DIR = r"C:\Users\zy187\JaspersoftWorkspace\JasperReportsSamples"
+    import sys
-    TARGET_DIR = os.path.join(os.path.dirname(__file__), "jrxml_source")
+
+    if len(sys.argv) >= 2:
+        SOURCE_DIR = sys.argv[1]
+    else:
+        SOURCE_DIR = os.environ.get(
+            "JRXML_COLLECT_SOURCE",
+            r"C:\Users\zy187\JaspersoftWorkspace\JasperReportsSamples"
+        )
+
+    TARGET_DIR = str(JRXML_SOURCE_DIR)
     
     if not os.path.exists(SOURCE_DIR):
         print(f"错误：源目录不存在 - {SOURCE_DIR}")
-        print("请检查路径是否正确")
+        print("请检查路径是否正确，或通过命令行参数指定:")
+        print(f"  python collect_jrxml.py <源目录路径>")
         exit(1)
     
     collect_jrxml_files(SOURCE_DIR, TARGET_DIR)

config.py

@@ -0,0 +1,138 @@
+"""
+config.py
+统一配置管理，从 .env 文件读取环境变量
+所有脚本通过此模块获取配置，避免硬编码
+"""
+
+import os
+from pathlib import Path
+
+
+def _get_project_root() -> Path:
+    return Path(__file__).resolve().parent
+
+
+def _load_dotenv():
+    """简易 .env 文件加载器，不依赖 python-dotenv"""
+    project_root = _get_project_root()
+    env_file = project_root / ".env"
+
+    if not env_file.exists():
+        return
+
+    with open(env_file, "r", encoding="utf-8") as f:
+        for line in f:
+            line = line.strip()
+            if not line or line.startswith("#"):
+                continue
+            if "=" not in line:
+                continue
+            key, _, value = line.partition("=")
+            key = key.strip()
+            value = value.strip().strip('"').strip("'")
+            if key and key not in os.environ:
+                os.environ[key] = value
+
+
+_load_dotenv()
+
+PROJECT_ROOT = _get_project_root()
+
+
+def _get_path(key: str, default: str) -> Path:
+    value = os.environ.get(key, default)
+    p = Path(value)
+    if not p.is_absolute():
+        p = PROJECT_ROOT / p
+    return p
+
+
+def _get_str(key: str, default: str) -> str:
+    return os.environ.get(key, default)
+
+
+def _get_bool(key: str, default: bool) -> bool:
+    value = os.environ.get(key, "").strip().lower()
+    if value in ("true", "1", "yes"):
+        return True
+    if value in ("false", "0", "no"):
+        return False
+    return default
+
+
+def _get_int(key: str, default: int) -> int:
+    try:
+        return int(os.environ.get(key, str(default)))
+    except ValueError:
+        return default
+
+
+def _get_float(key: str, default: float) -> float:
+    try:
+        return float(os.environ.get(key, str(default)))
+    except ValueError:
+        return default
+
+
+# ==================== 模型配置 ====================
+EMBEDDING_MODEL_NAME = _get_str("EMBEDDING_MODEL_NAME", "Qwen/Qwen3-Embedding-4B")
+EMBEDDING_MODEL_PATH = _get_path("EMBEDDING_MODEL_PATH", "models/Qwen3-Embedding-4B")
+HF_ENDPOINT = _get_str("HF_ENDPOINT", "https://hf-mirror.com")
+
+# ==================== 硬件配置 ====================
+USE_GPU = _get_bool("USE_GPU", True)
+USE_FP16 = _get_bool("USE_FP16", True)
+BATCH_SIZE = _get_int("BATCH_SIZE", 64)
+
+# ==================== 目录配置 ====================
+JRXML_SOURCE_DIR = _get_path("JRXML_SOURCE_DIR", "jrxml_source")
+CHUNKER_OUTPUT_DIR = _get_path("CHUNKER_OUTPUT_DIR", "jrxml_chunker_output")
+EMBEDDINGS_DIR = _get_path("EMBEDDINGS_DIR", "embeddings")
+CHROMA_DB_PATH = _get_path("CHROMA_DB_PATH", "chroma_db")
+CHROMA_COLLECTION_NAME = _get_str("CHROMA_COLLECTION_NAME", "jrxml_chunks")
+
+# ==================== 分块配置 ====================
+MAX_CHUNK_SIZE = _get_int("MAX_CHUNK_SIZE", 2000)
+
+# ==================== 查询配置 ====================
+DEFAULT_N_RESULTS = _get_int("DEFAULT_N_RESULTS", 5)
+SIMILARITY_THRESHOLD = _get_float("SIMILARITY_THRESHOLD", 0.3)
+
+
+def resolve_model_path() -> str:
+    """
+    解析模型路径：
+    1. 如果 EMBEDDING_MODEL_PATH 本地存在，使用本地路径
+    2. 否则使用 EMBEDDING_MODEL_NAME 作为 Hub 模型名
+    """
+    if EMBEDDING_MODEL_PATH.exists():
+        return str(EMBEDDING_MODEL_PATH)
+    return EMBEDDING_MODEL_NAME
+
+
+def print_config():
+    """打印当前配置（调试用）"""
+    print(f"{'='*60}")
+    print(f"JRXML RAG 当前配置")
+    print(f"{'='*60}")
+    print(f"  项目根目录:       {PROJECT_ROOT}")
+    print(f"  嵌入模型名称:     {EMBEDDING_MODEL_NAME}")
+    print(f"  嵌入模型路径:     {EMBEDDING_MODEL_PATH}")
+    print(f"  模型解析结果:     {resolve_model_path()}")
+    print(f"  HF 镜像:          {HF_ENDPOINT}")
+    print(f"  GPU 加速:         {USE_GPU}")
+    print(f"  FP16 半精度:      {USE_FP16}")
+    print(f"  批处理大小:       {BATCH_SIZE}")
+    print(f"  JRXML 源目录:     {JRXML_SOURCE_DIR}")
+    print(f"  分块输出目录:     {CHUNKER_OUTPUT_DIR}")
+    print(f"  向量输出目录:     {EMBEDDINGS_DIR}")
+    print(f"  Chroma 数据库:    {CHROMA_DB_PATH}")
+    print(f"  Chroma 集合名:    {CHROMA_COLLECTION_NAME}")
+    print(f"  最大 Chunk 大小:  {MAX_CHUNK_SIZE}")
+    print(f"  默认返回结果数:   {DEFAULT_N_RESULTS}")
+    print(f"  相似度阈值:       {SIMILARITY_THRESHOLD}")
+    print(f"{'='*60}")
+
+
+if __name__ == "__main__":
+    print_config()

down_embedding_model.py

@@ -1,26 +1,26 @@
 """
 down_embedding_model.py
-下载 Qwen3-Embedding-4B 嵌入模型
+下载嵌入模型（模型名称通过 .env / config.py 配置）
 """
 
 import os
 import sys
 from pathlib import Path
+from config import EMBEDDING_MODEL_NAME, EMBEDDING_MODEL_PATH, HF_ENDPOINT
 
 def download_model():
-    """下载 Qwen3-Embedding-4B 模型"""
+    """下载嵌入模型"""
-    project_root = Path(__file__).resolve().parent
+    model_dir = EMBEDDING_MODEL_PATH
-    model_dir = project_root / "models" / "Qwen3-Embedding-4B"
 
     print("=" * 60)
-    print("Qwen3-Embedding-4B 模型下载")
+    print(f"{EMBEDDING_MODEL_NAME} 模型下载")
     print("=" * 60)
+    print(f"模型名称: {EMBEDDING_MODEL_NAME}")
     print(f"模型目录: {model_dir}")
     print()
 
-    # 使用国内镜像加速
+    os.environ['HF_ENDPOINT'] = HF_ENDPOINT
-    os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
+    print(f"使用 HuggingFace 镜像: {HF_ENDPOINT}")
-    print("使用 HuggingFace 镜像: https://hf-mirror.com")
     print()
 
     try:
@@ -34,13 +34,13 @@ def download_model():
     # 创建模型目录
     os.makedirs(model_dir, exist_ok=True)
 
-    print(f"开始下载 Qwen3-Embedding-4B 模型...")
+    print(f"开始下载 {EMBEDDING_MODEL_NAME} 模型...")
-    print(f"模型大小约 4GB，请耐心等待...")
+    print(f"请耐心等待...")
     print()
 
     try:
         snapshot_download(
-            repo_id="Qwen/Qwen3-Embedding-4B",
+            repo_id=EMBEDDING_MODEL_NAME,
             local_dir=str(model_dir),
             local_dir_use_symlinks=False,
             resume_download=True

embed_chunks.py

@@ -1,7 +1,7 @@
 """
 embed_chunks.py
-使用本地 Qwen3-Embedding-4B 模型对 JRXML chunks 进行向量化
+使用嵌入模型对 JRXML chunks 进行向量化
-支持 GPU (CUDA) 或 CPU
+支持 GPU (CUDA) 或 CPU，模型通过 .env / config.py 配置
 """
 
 import os
@@ -12,6 +12,10 @@ from pathlib import Path
 import numpy as np
 import torch
 from sentence_transformers import SentenceTransformer
+from config import (
+    EMBEDDING_MODEL_PATH, CHUNKER_OUTPUT_DIR, EMBEDDINGS_DIR,
+    USE_FP16, BATCH_SIZE, resolve_model_path
+)
 
 def build_text_for_embedding(chunk: dict) -> str:
     """
@@ -48,8 +52,8 @@ def build_text_for_embedding(chunk: dict) -> str:
 
 
 def main(chunks_json_path: str = None, output_dir: str = None,
-         model_path: str = None, batch_size: int = 64, normalize: bool = True,
+         model_path: str = None, batch_size: int = None, normalize: bool = True,
-         use_fp16: bool = True):
+         use_fp16: bool = None):
     """
     主流程：
     1. 加载 chunk JSON
@@ -60,19 +64,25 @@ def main(chunks_json_path: str = None, output_dir: str = None,
     project_root = Path(__file__).resolve().parent
 
     if chunks_json_path is None:
-        chunks_json_path = project_root / "jrxml_chunker_output" / "all_chunks.json"
+        chunks_json_path = CHUNKER_OUTPUT_DIR / "all_chunks.json"
     else:
         chunks_json_path = Path(chunks_json_path)
 
     if output_dir is None:
-        output_dir = project_root / "embeddings"
+        output_dir = EMBEDDINGS_DIR
     else:
         output_dir = Path(output_dir)
 
     if model_path is None:
-        model_path = project_root / "models" / "Qwen3-Embedding-4B"
+        model_path = resolve_model_path()
     else:
-        model_path = Path(model_path)
+        model_path = str(model_path)
+
+    if batch_size is None:
+        batch_size = BATCH_SIZE
+
+    if use_fp16 is None:
+        use_fp16 = USE_FP16
 
     if not chunks_json_path.exists():
         print(f"❌ Chunks 文件不存在: {chunks_json_path}")
@@ -91,19 +101,16 @@ def main(chunks_json_path: str = None, output_dir: str = None,
     print(f"\n🧠 加载嵌入模型: {model_path}")
     print(f"   设备: {device}")
 
-    # 检查是否是 HuggingFace Hub 模型（格式为 username/model_name）
     model_path_str = str(model_path)
-    # Windows PowerShell 会把 / 自动转成 \，需要还原
     if "\\" in model_path_str and not os.path.exists(model_path_str):
         model_path_str = model_path_str.replace("\\", "/")
 
     is_hub_model = "/" in model_path_str and not os.path.exists(model_path_str)
 
-    # 如果是本地路径但不存在，则报错
     if not is_hub_model and not os.path.exists(model_path_str):
         print(f"❌ 模型目录不存在: {model_path}")
-        print(f"   请先下载模型到 {model_path}")
+        print(f"   请先运行 down_embedding_model.py 下载模型")
-        print(f"   或者使用 HuggingFace Hub 模型，例如: sentence-transformers/all-MiniLM-L6-v2")
+        print(f"   或在 .env 中配置 EMBEDDING_MODEL_NAME 为 Hub 模型名")
         return None
 
     model = SentenceTransformer(model_path_str, device=device)
@@ -183,17 +190,17 @@ def main(chunks_json_path: str = None, output_dir: str = None,
 if __name__ == "__main__":
     import argparse
     project_root = Path(__file__).resolve().parent
-    default_chunks = project_root / "jrxml_chunker_output" / "all_chunks.json"
+    default_chunks = CHUNKER_OUTPUT_DIR / "all_chunks.json"
 
     parser = argparse.ArgumentParser(description="JRXML Chunks 向量化工具")
     parser.add_argument("chunks_json", nargs="?", default=str(default_chunks),
                         help=f"Chunks JSON 文件路径 (默认: {default_chunks})")
     parser.add_argument("--output_dir", "-o", default=None,
-                        help="输出目录 (默认: embeddings)")
+                        help=f"输出目录 (默认: {EMBEDDINGS_DIR})")
     parser.add_argument("--model_path", "-m", default=None,
-                        help="模型路径 (默认: models/Qwen3-Embedding-4B)")
+                        help=f"模型路径 (默认: {resolve_model_path()})")
-    parser.add_argument("--batch_size", "-b", type=int, default=64,
+    parser.add_argument("--batch_size", "-b", type=int, default=BATCH_SIZE,
-                        help="批处理大小 (默认: 64)")
+                        help=f"批处理大小 (默认: {BATCH_SIZE})")
     parser.add_argument("--no_normalize", action="store_true",
                         help="不做向量归一化")
     parser.add_argument("--no_fp16", action="store_true",

import_to_chroma.py

@@ -11,11 +11,12 @@ from pathlib import Path
 import numpy as np
 import chromadb
 from tqdm import tqdm
+from config import EMBEDDINGS_DIR, CHROMA_DB_PATH, CHROMA_COLLECTION_NAME
 
 
 def main(embeddings_dir: str = None,
          chroma_path: str = None,
-         collection_name: str = "jrxml_chunks"):
+         collection_name: str = None):
     """
     从 embeddings 目录读取向量和 chunks，导入 Chroma 持久化数据库
 
@@ -27,15 +28,18 @@ def main(embeddings_dir: str = None,
     project_root = Path(__file__).resolve().parent
 
     if embeddings_dir is None:
-        embeddings_dir = project_root / "embeddings"
+        embeddings_dir = EMBEDDINGS_DIR
     else:
         embeddings_dir = Path(embeddings_dir)
 
     if chroma_path is None:
-        chroma_path = project_root / "chroma_db"
+        chroma_path = CHROMA_DB_PATH
     else:
         chroma_path = Path(chroma_path)
 
+    if collection_name is None:
+        collection_name = CHROMA_COLLECTION_NAME
+
     embeddings_file = embeddings_dir / "embeddings.npy"
     chunks_file = embeddings_dir / "chunks.json"
 
@@ -164,11 +168,11 @@ if __name__ == "__main__":
     import argparse
     parser = argparse.ArgumentParser(description="JRXML Chunks 导入 Chroma 工具")
     parser.add_argument("--embeddings_dir", "-e", default=None,
-                        help="向量文件目录 (默认: embeddings)")
+                        help=f"向量文件目录 (默认: {EMBEDDINGS_DIR})")
     parser.add_argument("--chroma_path", "-c", default=None,
-                        help="Chroma 数据库路径 (默认: chroma_db)")
+                        help=f"Chroma 数据库路径 (默认: {CHROMA_DB_PATH})")
-    parser.add_argument("--collection_name", "-n", default="jrxml_chunks",
+    parser.add_argument("--collection_name", "-n", default=CHROMA_COLLECTION_NAME,
-                        help="集合名称 (默认: jrxml_chunks)")
+                        help=f"集合名称 (默认: {CHROMA_COLLECTION_NAME})")
 
     args = parser.parse_args()
 

jrxml_banch_chunker.py

@@ -11,9 +11,10 @@ from pathlib import Path
 from datetime import datetime
 from collections import defaultdict
 from jrxml_chunker import JRXMLSemanticChunker, save_chunks_to_json, print_chunk_summary
+from config import JRXML_SOURCE_DIR, CHUNKER_OUTPUT_DIR, MAX_CHUNK_SIZE
 
 
-def batch_chunk_with_report(input_dir: str, output_dir: str = None, max_chunk_size: int = 2000):
+def batch_chunk_with_report(input_dir: str = None, output_dir: str = None, max_chunk_size: int = None):
     """
     批量分块并生成详细报告
     
@@ -22,6 +23,8 @@ def batch_chunk_with_report(input_dir: str, output_dir: str = None, max_chunk_si
         output_dir: 输出目录，默认为 input_dir/../chunked_output
         max_chunk_size: 单个chunk最大字节数
     """
+    if input_dir is None:
+        input_dir = str(JRXML_SOURCE_DIR)
     input_path = Path(input_dir).resolve()
     
     if not input_path.exists():
@@ -32,12 +35,14 @@ def batch_chunk_with_report(input_dir: str, output_dir: str = None, max_chunk_si
         print(f"❌ 不是目录: {input_path}")
         return None
     
-    # 设置输出目录
     if output_dir is None:
-        output_dir = input_path.parent / f"{input_path.name}_chunked_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
+        output_dir = str(CHUNKER_OUTPUT_DIR)
     output_path = Path(output_dir)
     output_path.mkdir(parents=True, exist_ok=True)
 
+    if max_chunk_size is None:
+        max_chunk_size = MAX_CHUNK_SIZE
+    
     print(f"\n{'='*60}")
     print(f"JRXML 语义分块 v3.0 - 批量处理")
     print(f"{'='*60}")
@@ -214,21 +219,24 @@ if __name__ == "__main__":
         print("=" * 60)
         print("JRXML Semantic Chunking v3.0 - 批量处理工具")
         print("=" * 60)
+        print(f"\n默认输入目录: {JRXML_SOURCE_DIR}")
+        print(f"默认输出目录: {CHUNKER_OUTPUT_DIR}")
         print("\n用法:")
-        print("  python batch_chunker.py <目录路径>")
+        print("  python jrxml_banch_chunker.py <目录路径>")
-        print("  python batch_chunker.py <文件路径>")
+        print("  python jrxml_banch_chunker.py <文件路径>")
+        print("  python jrxml_banch_chunker.py                  (使用默认配置)")
         print("\n参数:")
         print("  <路径>            JRXML文件所在目录 或 单个JRXML文件路径")
         print("  --output <目录>   指定输出目录 (可选)")
         print("\n示例:")
-        print("  python batch_chunker.py ./jasper_reports")
+        print("  python jrxml_banch_chunker.py")
-        print("  python batch_chunker.py ./jasper_reports --output ./chunks")
+        print("  python jrxml_banch_chunker.py ./jasper_reports")
-        print("  python batch_chunker.py report.jrxml")
+        print("  python jrxml_banch_chunker.py ./jasper_reports --output ./chunks")
+        print("  python jrxml_banch_chunker.py report.jrxml")
         sys.exit(0)
     
     input_path = sys.argv[1]
     
-    # 解析--output参数
     output_dir = None
     if "--output" in sys.argv:
         idx = sys.argv.index("--output")
@@ -236,10 +244,8 @@ if __name__ == "__main__":
             output_dir = sys.argv[idx + 1]
     
     if os.path.isdir(input_path):
-        # 批量处理目录
         batch_chunk_with_report(input_path, output_dir)
     elif os.path.isfile(input_path):
-        # 处理单个文件
         chunk_single_file_with_report(input_path, output_dir)
     else:
         print(f"❌ 路径无效: {input_path}")

query_chroma.py

@@ -2,6 +2,7 @@
 query_chroma.py
 查询 Chroma 数据库，从自然语言查找相关 JRXML chunk
 支持命令行单次查询和交互式连续查询
+模型通过 .env / config.py 配置
 """
 
 import os
@@ -12,19 +13,27 @@ import numpy as np
 import torch
 from sentence_transformers import SentenceTransformer
 import chromadb
+from config import (
+    CHROMA_DB_PATH, CHROMA_COLLECTION_NAME, USE_FP16,
+    DEFAULT_N_RESULTS, SIMILARITY_THRESHOLD, resolve_model_path
+)
 
 
 class JRXMLSearcher:
     def __init__(self, chroma_path: str = None,
-                 collection_name: str = "jrxml_chunks",
+                 collection_name: str = None,
                  model_path: str = None,
-                 use_fp16: bool = True):
+                 use_fp16: bool = None):
         project_root = Path(__file__).resolve().parent
 
         if chroma_path is None:
-            chroma_path = str(project_root / "chroma_db")
+            chroma_path = str(CHROMA_DB_PATH)
+        if collection_name is None:
+            collection_name = CHROMA_COLLECTION_NAME
         if model_path is None:
-            model_path = str(project_root / "models" / "Qwen3-Embedding-4B")
+            model_path = resolve_model_path()
+        if use_fp16 is None:
+            use_fp16 = USE_FP16
 
         # 处理 Hub 模型名称
         model_path_str = str(model_path)
@@ -110,13 +119,13 @@ def main():
     parser.add_argument("query", nargs="?", default="",
                         help="搜索关键词（不提供则进入交互模式）")
     parser.add_argument("--chroma_path", "-c", default=None,
-                        help=f"Chroma 数据库路径 (默认: chroma_db)")
+                        help=f"Chroma 数据库路径 (默认: {CHROMA_DB_PATH})")
-    parser.add_argument("--collection", "-n", default="jrxml_chunks",
+    parser.add_argument("--collection", "-n", default=CHROMA_COLLECTION_NAME,
                         help="集合名称")
     parser.add_argument("--model_path", "-m", default=None,
                         help="嵌入模型路径")
-    parser.add_argument("--n_results", "-k", type=int, default=5,
+    parser.add_argument("--n_results", "-k", type=int, default=DEFAULT_N_RESULTS,
-                        help="返回结果数 (默认: 5)")
+                        help=f"返回结果数 (默认: {DEFAULT_N_RESULTS})")
     parser.add_argument("--filter_field", "-f",
                         help="按 chunk_type 过滤，例如: field, query, chart")
     parser.add_argument("--threshold", "-t", type=float,
@@ -127,14 +136,10 @@ def main():
     args = parser.parse_args()
 
     if args.chroma_path is None:
-        args.chroma_path = str(project_root / "chroma_db")
+        args.chroma_path = str(CHROMA_DB_PATH)
 
     if args.model_path is None:
-        default_model = project_root / "models" / "Qwen3-Embedding-4B"
+        args.model_path = resolve_model_path()
-        if not default_model.exists():
-            args.model_path = "sentence-transformers/all-MiniLM-L6-v2"
-        else:
-            args.model_path = str(default_model)
 
     # 检查数据库
     if not os.path.exists(args.chroma_path):

setup.bat

@@ -0,0 +1,103 @@
+@echo off
+chcp 65001 >nul
+setlocal enabledelayedexpansion
+
+:: ============================================================
+:: JRXML RAG 项目 - Windows 一键部署脚本
+:: ============================================================
+
+title JRXML RAG 环境部署
+
+echo.
+echo ============================================================
+echo   JRXML RAG 项目 - 环境部署脚本
+echo ============================================================
+echo.
+
+:: 检查 Python
+echo [1/5] 检查 Python 环境...
+python --version >nul 2>&1
+if %errorlevel% neq 0 (
+    echo [错误] 未找到 Python，请先安装 Python 3.11+
+    pause
+    exit /b 1
+)
+for /f "tokens=2" %%v in ('python --version 2^>^&1') do echo    Python 版本: %%v
+
+:: 检查 CUDA
+echo.
+echo [2/5] 检查 CUDA 环境...
+python -c "import torch; print(f'   PyTorch: {torch.__version__}'); print(f'   CUDA 可用: {torch.cuda.is_available()}'); print(f'   GPU: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else \"N/A\"}')" 2>nul
+if %errorlevel% neq 0 (
+    echo    PyTorch 未安装，将在后续步骤安装
+)
+
+:: 初始化 .env 配置
+echo.
+echo [3/5] 初始化环境配置...
+if not exist ".env" (
+    if exist ".env.example" (
+        copy ".env.example" ".env" >nul
+        echo    已从 .env.example 创建 .env 配置文件
+        echo    请根据需要编辑 .env 文件修改配置
+    ) else (
+        echo    [警告] 未找到 .env.example，跳过配置初始化
+    )
+) else (
+    echo    .env 配置文件已存在，跳过
+)
+
+:: 创建必要目录
+echo.
+echo [4/5] 创建项目目录...
+set "DIRS=jrxml_source jrxml_chunker_output models embeddings chroma_db"
+for %%d in (%DIRS%) do (
+    if not exist "%%d" (
+        mkdir "%%d" >nul 2>&1
+        echo    创建目录: %%d
+    )
+)
+echo    目录结构已就绪
+
+:: 安装依赖
+echo.
+echo [5/5] 安装 Python 依赖...
+echo.
+
+:: 检测是否有 NVIDIA GPU
+python -c "import subprocess; exit(0 if subprocess.run(['nvidia-smi'], capture_output=True).returncode == 0 else 1)" 2>nul
+if %errorlevel% equ 0 (
+    set "HAS_GPU=1"
+    echo    [检测到 NVIDIA GPU，安装 CUDA 版 PyTorch]
+    echo.
+    echo    安装 PyTorch (CUDA 版本)...
+    pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124
+) else (
+    set "HAS_GPU=0"
+    echo    [未检测到 GPU，安装 CPU 版 PyTorch]
+    echo.
+    echo    安装 PyTorch (CPU 版本)...
+    pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
+)
+
+echo.
+echo    安装核心依赖...
+pip install sentence-transformers chromadb numpy tqdm huggingface_hub
+
+echo.
+echo ============================================================
+echo   部署完成！
+echo ============================================================
+echo.
+echo   下一步操作:
+echo     1. 编辑 .env 配置文件（可选）
+echo     2. 收集 JRXML 文件:   python collect_jrxml.py
+echo     3. 语义分块:          python jrxml_banch_chunker.py
+echo     4. 下载嵌入模型:      python down_embedding_model.py
+echo     5. 向量化:            python embed_chunks.py
+echo     6. 导入 Chroma:       python import_to_chroma.py
+echo     7. 开始查询:          python query_chroma.py
+echo.
+echo   查看当前配置:           python config.py
+echo.
+pause